1. Field of the Invention
The present disclosure relates generally to a device and method for filtering electrical signals, in particular but not exclusively acoustic signals. Embodiments of the invention can however be applied also to radio frequency signals, for instance, signals coming from antenna arrays, to biomedical signals, and to signals used in geology.
2. Description of the Related Art
As is known, in systems designed for receiving signals propagating in a physical medium, the picked signals comprise, in addition to the useful signal, undesired components. The undesired components may be any type of noise (white noise, flicker noise, etc.) or other types of acoustic signals superimposed on the useful signal.
If the useful signal and the interfering signal occupy the same time frequency band, time filtering cannot be used to separate them. Nevertheless, the useful signal and the interference signal normally arise from different locations in space. Spatial separation may therefore be exploited to separate the useful signal from the interference signals. Spatial separation is obtained through a spatial filter, i.e., a filter based upon an array of sensors.
Linear filtering techniques are currently used in signal processing in order to carry out spatial filtering. Such techniques are, for instance, applied in the following fields:                radar (e.g., control of air traffic);        sonar (location and classification of the source);        communications (e.g., transmission of sectors in satellite communications);        astrophysical exploration (high resolution representation of the universe);        biomedical applications (e.g., hearing aids).        
By arranging different sensors in different locations in space, various spatial samples of one and the same signal are obtained.
Various spatial filtering techniques are known to the art. The simplest one is referred to as “delay-and-sum beamforming.” According to this technique, the set of sensor outputs, picked at a given instant, has a similar role as consecutive tap inputs in a transverse filter. In this connection see B. D. Van Veen, K. M. Buckley “Beamforming: A Versatile Approach to Spatial Filtering,” IEEE ASSP MAGAZINE, Apr. 1998, pages 4–24.
The most widely known filtering technique is referred to as “multiple sidelobe canceling.” According to this technique, 2N+1 sensors are arranged in appropriately chosen positions, linked to the direction of interest, and a particular beam of the set is identified as main beam, while the remaining beams are considered as auxiliary beams. The auxiliary beams are weighted by the multiple sidelobe canceller, so as to form a canceling beam which is subtracted from the main beam. The resultant estimated error is sent back to the multiple sidelobe canceller in order to check the corrections applied to its adjustable weights.
The most recent beamformers carry out adaptive filtering. This involves calculation of the autocorrelation matrix for the input signals. Various techniques are used for calculating the taps of the FIR filters at each sensor. Such techniques are aimed at optimizing a given physical quantity. If the aim is to optimize the signal-to-noise ratio, it is necessary to calculate the self-values or “eigenvalues” of the autocorrelation matrix. If the response in a given direction is set equal to 1, it is necessary to carry out a number of matrix operations. Consequently, all these techniques involve a large number of calculations, which increases with the number of sensors.
Another problem that afflicts the spatial filtering systems that have so far been proposed is linked to detecting changes in environmental noise and clustering of sounds and acoustic scenarios. This problem can be solved using fuzzy logic techniques. In fact, pure tones are hard to find in nature; more frequently, mixed sounds are found that have an arbitrary power spectral density. The human brain separates one sound from another in a very short time. The separation of one sound from another is rather slow if performed automatically.
According to existing studies, the human brain performs a recognition of the acoustic scenario in two ways: in a time frequency plane, the tones are clustered if they are close together either in time or in frequency.
Clustering techniques based upon fuzzy logic are known in the literature. The starting point is time frequency analysis. For each time frequency element in this representation, a plurality of features is extracted, which characterize the elements in the time frequency region of interest. Clustering of the elements according to these premises enables assignment of each auditory stream to a given cluster in the time frequency plane.
Other techniques known in the literature tend to achieve discrimination of sounds via analysis of the frequency content. For this purpose, techniques for evaluating the content of harmonics are used, such as measurement of lack of harmony, bandwidth, etc.